Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/24893
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dc.contributor.authorAlkarkhi, A.F.M.-
dc.contributor.authorAlqaraghuli, W.A.A.-
dc.contributor.authorMohamed Zam, N.R.-
dc.contributor.authorManan, D.M.A.-
dc.contributor.authorMahmud, M.N.-
dc.contributor.authorHuda, N.-
dc.contributor.authorUniKL BiS-
dc.date.accessioned2021-05-03T06:00:10Z-
dc.date.available2021-05-03T06:00:10Z-
dc.date.issued2020-03-
dc.identifier.citationAlkarkhi, A. F. M., Alqaraghuli, W. A. A., Mohamed Zam, N. R., Manan, D. M. A., Mahmud, M. N., & Huda, N. (2020). Differentiation of ripe and unripe fruit flour using mineral composition data—Statistical assessment. Data in Brief, 30. https://doi.org/10.1016/j.dib.2020.105414en_US
dc.identifier.issn23523409-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S2352340920303085-
dc.identifier.urihttp://hdl.handle.net/123456789/24893-
dc.descriptionThis articles is index by Scopusen_US
dc.description.abstractData on the mineral composition and content of one heavy metal measured in three different fruit flours prepared from ripe and unripe fruits (pulp and peel) are presented. The mineral composition (sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), zinc (Zn), copper (Cu), iron (Fe) and manganese (Mn)) and content of one heavy metal (lead (Pb)) of the flours were analyzed by atomic absorption spectrophotometry. The analysis showed that the data can be used for differentiation between different fruits and stages of ripeness, as revealed by discriminant analysis and cluster analysis. The data provided can be used by researchers and scientists in the differentiation of fruits based on major and minor mineral elements.en_US
dc.publisherElsevier Inc.en_US
dc.subjectMajor elementen_US
dc.subjectMinor elementen_US
dc.subjectHeavy metalen_US
dc.subjectDiscriminant analysisen_US
dc.subjectCluster analysisen_US
dc.titleDifferentiation of ripe and unripe fruit flour using mineral composition data—Statistical assessmenten_US
dc.typeArticleen_US
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